Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning

Alexander A Ksenofontov; Yaroslav I Isaev; Michail M Lukanov; Dmitry M Makarov; Varvara A Eventova; Ilya A Khodov; Mechail B Berezin

doi:10.1039/d3cp00253e

Accurate prediction of ¹¹B NMR chemical shift of BODIPYs via machine learning

Phys Chem Chem Phys. 2023 Mar 29;25(13):9472-9481. doi: 10.1039/d3cp00253e.

Authors

Alexander A Ksenofontov¹, Yaroslav I Isaev^{1

2}, Michail M Lukanov¹, Dmitry M Makarov¹, Varvara A Eventova^{1

2}, Ilya A Khodov¹, Mechail B Berezin¹

Affiliations

¹ G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia. ivalex.09@mail.ru.
² Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia.

PMID: 36935644
DOI: 10.1039/d3cp00253e

Abstract

In this article, we present the results of developing a model based on an RFR machine learning method using the ISIDA fragment descriptors for predicting the ¹¹B NMR chemical shift of BODIPYs. The model is freely available at https://ochem.eu/article/146458. The model demonstrates the high quality of predicting the ¹¹B NMR chemical shift (RMSE, 5CV (FINALE training set) = 0.40 ppm, RMSE (TEST set) = 0.14 ppm). In addition, we compared the "cost" and the user-friendliness for calculations using the quantum-chemical model with the DFT/GIAO approach. The ¹¹B NMR chemical shift prediction accuracy (RMSE) of the model considered is more than three times higher and tremendously faster than the DFT/GIAO calculations. As a result, we provide a convenient tool and database that we collected for all researchers, that allows them to predict the ¹¹B NMR chemical shift of boron-containing dyes. We believe that the new model will make it easier for researchers to correctly interpret the ¹¹B NMR chemical shifts experimentally determined and to select more optimal conditions to perform an NMR experiment.